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This article was published as a part of the Data Science Blogathon


Let’s start our discussion by imagine a scenario where you have tested your machine learning model well, and you get absolutely perfect accuracy. After getting the accuracy, you are happy with your work and say well done to yourself, and then decide to deploy your project. However, when the actual data is applied to this model in the production, you get poor results. So, you think that why did this happen and how to fix it?

The possible reason for this occurrence is Data Leakage. It is one of the leading machine learning errors. Data leakage in machine learning happens when the data that we are used to training a machine learning algorithm is having the information which the model is trying to predict, this results in unreliable and bad prediction outcomes after model deployment.

                                                Image Source: Link

So, In this article, we will discuss all the things related to Data Leakage including what it is, how it has happened, how to fix it, etc. So, If you are a Data Science enthusiast, then read this article completely since it is one of the most important concepts that you as a Data Science enthusiast must know to accelerate your Data Science Journey.

Table of Contents

The topics which we are going to discuss in this detailed article on Data Leakage are as follows:

What is meant by Data Leakage?

How does Data Leakage exactly happen?

What are the examples of Data Leakage?

How to detect Data Leakage?

How to Fix the Problem of Data Leakage?

What is meant by Data Leakage?

Data Leakage is the scenario where the Machine Learning Model is already aware of some part of test data after chúng tôi causes the problem of overfitting.

In Machine learning, Data Leakage refers to a mistake that is made by the creator of a machine learning model in which they accidentally share the information between the test and training data sets. Typically, when splitting a data set into testing and training sets, the goal is to ensure that no data is shared between these two sets. Ideally, there is no intersection between these two sets. This is because the purpose of the testing set is to simulate the real-world data which is unseen to that model. However, when evaluating a model, we do have full access to both our train and test sets, so it is our duty to ensure that there is no overlapping between the training data and the testing data (i.e, no intersection).

How does it exactly happen?

Let’s discuss the happening of data leakage problem in a much detailed manner:

When you split your data into training and testing subsets, some of your data present in the test set is also copied in the train set and vice-versa.

As a result of which when you train your model with this type of split it will give really good results on the train and test set i.e, both training and testing accuracy should be high.

But when you deploy your model into production it will not perform well, because when a new type of data comes in it won’t be able to handle it.

Examples of Data Leakage

In this section, we will discuss some of the example scenarios where the problem of data leakage occurs. After understanding these examples, you have better clarity about the problem of Data Leakage.

General Examples of Data Leakage

To understand this example, firstly we have to understand the difference between “Target Variable” and “Features” in Machine learning.

Target variable: The Output which the model is trying to predict.

Features: The data used by the model to predict the target variable.

Example 1-

The most obvious and easy-to-understand cause of data leakage is to include the target variable as a feature. What happens is that after including the target variable as a feature, our purpose of prediction got destroyed. This is likely to be done by mistake but while modelling any ML model, you have to make sure that the target variable is differentiated from the set of features.

Example 2 –

To properly evaluate a particular machine learning model, we split our available data into training and test subsets. Invariably, what happens is that some of the information from the test set is shared with the train set, and vice-versa. So, another common cause of data leakage is to include test data with training data. Therefore, It becomes necessary to test the models with new and previously unseen data. If we include the test data in the training, then the process would defeat this purpose.

In real-life problem statements, the above two cases are not very likely to occur because they can easily be spotted while doing the modelling. So, now let’s see some more dangerous causes of data leakage that can sneak.

Presence of Giveaway features

Giveaway features are those features from the set of all features that expose the information about the target variable and would not be available after the model is deployed.

Let’s consider this with the help of the following examples:

Example 1 – 

Let’s we are working on a problem statement in which we have to build a model that predicts a certain medical condition. If we have a feature that indicates whether a patient had a surgery related to that medical condition, then it causes data leakage and we should never be included that as a feature in the training data. The indication of surgery is highly predictive of the medical condition and would probably not be available in all cases. If we already know that a patient had a surgery related to a medical condition, then we may not even require a predictive model to start with.

Example 2 – 

Let’s we are working on a problem statement in which we have to build a model that predicts if a user will stay on a website. Including features that expose the information about future visits will cause the problem of data leakage. So, we have to use only features about the current session because information about the future sessions is not generally available after we deployed our model.

Leakage during Data preprocessing

While solving a Machine learning problem statement, firstly we do the data cleaning and preprocessing which involves the following steps:

Evaluating the parameters for normalizing or rescaling features

Finding the minimum and maximum values of a particular feature

Normalize the particular feature in our dataset

Removing the outliers

Fill or completely remove the missing data in our dataset

The above-described steps should be done using only the training set. If we use the entire dataset to perform these operations, data leakage may occur. Applying preprocessing techniques to the entire dataset will cause the model to learn not only the training set but also the test set. As we all know that the test set should be new and previously unseen for any model.

How to detect Data Leakage?

Let’s consider the following three cases to detecting data leakage:

Case-1: Case-2: 

While doing the Exploratory Data Analysis (EDA), we may detect features that are very highly correlated with the target variable. Of course, some features are more correlated than others but a surprisingly high correlation needs to be checked and handled carefully. We should pay close attention to those features. So, with the help of EDA, we can examine the raw data through statistical and visualization tools.


After the completion of the model training, if features are having very high weights, then we should pay close attention. Those features might be leaky.

How to fix the problem of Data Leakage?

The main culprit behind this is the way we split our dataset and when. The following steps can prove to be very crucial in preventing data leakage:

Idea-1 (Extracting the appropriate set of Features)

Figure showing the selection of best set of features for your ML Model

                                                        Image Source: Link

To fix the problem of data leakage, the first method we can try is to extract the appropriate set of features for a machine learning model. While choosing features, we should make sure that the given features are not correlated with the given target variable, as well as that they do not contain information about the target variable, which is not naturally available at the time of prediction.

Idea-2 (Create a Separate Validation Set)

Figure Showing splitting of the dataset into train, validation, and test subsets

                                                         Image Source: Link

To minimize or avoid the problem of data leakage, we should try to set aside a validation set in addition to training and test sets if possible. The purpose of the validation set is to mimic the real-life scenario and can be used as a final step. By doing this type of activity, we will identify if there is any possible case of overfitting which in turn can act as a caution warning against deploying models that are expected to underperform in the production environment.

Idea-3 (Apply Data preprocessing Separately to both Train and Test subsets)

Figure Showing How a Data can be divided into train and test subsets

                                                         Image Source: Link

While dealing with neural networks, it is a common practice that we normalize our input data firstly before feeding it into the model. Generally, data normalization is done by dividing the data by its mean value. More often than not, this normalization is applied to the overall data set, which influences the training set from the information of the test set and eventually it results in data leakage. Hence, to avoid data leakage, we have to apply any normalization technique separately to both training and test subsets.

Idea-4 (Time-Series Data)

Figure Showing an example of Time-Series Data

                                                            Image Source: Link

Problem with the Time-Series Type of data:

When dealing with time-series data, we should pay more attention to data leakage. For example, if we somehow use data from the future when doing computations for current features or predictions, it is highly likely to end up with a leaked model. It generally happens when the data is randomly split into train and test subsets. 

So, when working with time-series data, we put a cutoff value on time which might be very useful, as it prevents us from getting any information after the time of prediction.

Idea-5 (Cross-Validation)

Figure Showing Idea Behind Cross-Validation

                                                          Image Source: Link

When we have a limited amount of data to train our Machine learning algorithm, then it is a good practice to use cross-validation in the training process. What Cross-validation is doing is that it splits our complete data into k folds and iterates over the entire dataset in k number of times and each time we are using k-1 fold for training and 1 fold for testing our model.

To know more about Cross-Validation and its types, you can refer to the following article:

Detailed Discussion on Cross-Validation and its types


So, as a concluding step we can say that Data leakage is a widespread issue in the domain of predictive analytics. We train our different machine learning models with known data and expect the model to perform better predictions or results on previously unseen data in our production environment, which is our final aim. So, for a model to have a good performance in those predictions, it must generalize well. But Data leakage prevents a model to generalize well and thus causes some false assumptions about the model performance. Therefore, to create a robust and generalized predictive model, we should pay close attention to detect and avoid data leakage. This ends our today’s discussion on Data Leakage!

Congratulations on learning the most important concept of Machine Learning which you must know while working on real-life problems related to Data Science! 👏

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Creating An Ml Web App And Deploying It On Aws

This article was published as a part of the Data Science Blogathon.


Most data science projects deploy machine learning models as an on-demand prediction service or in batch prediction mode. Some modern applications deploy embedded models in edge and mobile devices. ML web app Model creation is easy but the ML model that you have been working on is of no use until and unless it is used in the real world and ready for production.

We’ll start with Model Deployment and why we should deploy the ML web app model on AWS. The article is chopped into parts this is because I’m trying to explain as simply as I can in order to make model deployment in AWS accessible for as many people as possible. So now let’s go!

What is Model Deployment?

Deployment is the method by which you integrate a machine learning model into an existing production environment to make practical business decisions based on data. It is one of the last stages in the machine learning life cycle and can be one of the most cumbersome.

What is AWS?

Services Provided by the AWS:-

Now as we have some basic understanding of Model deployment and AWS so now let’s dive into creating a website using ‘Streamlit library in Python.

Why Deploy your App on AWS?

Suppose that you have created an ML web app or any other app, that predicts the salary based upon the year of experience. But in order to reach that app to users/world so that users can use that app, you need a server(a server is nothing that provides services). In order to run any program/app, you need an OS. So in the server, an OS is running on which we deploy our code/app so that it can keep on running without having an actual computer/OS.

For getting a server we use multiple cloud services like AWS, GCP, AZURE, and many more. What these services do is that it provides us with a server for it is EC2 (Amazon Elastic Compute). EC2 provides an entirely new PC/server. A PC contains OS, network card, storage, and many types of things. In EC2 we just deploy our code so that our app will be running.

What is Streamlit?

Streamlit is a library in python that helps data scientist to deploy their models on websites. In simple terms, it is specifically made for data scientists as It helps them to create web apps for data science and machine learning in a short time.

Why use Streamlit?

With streamlit you can easily deploy your models and you don’t need to have knowledge of  Flask. With this, you can easily create a website with python in a few lines of code. As well you don’t need to have front-end knowledge like HTML, CSS, and JavaScript

Now let’s create a website using streamlit. 

The model is trained, so I have created the pickle file of the model so that I don’t have to, again and again, train the model. With the pickle file, your trained model is loaded and you will be using that model.

Let’s install streamlit library before creating a website:-


conda install -c conda-forge streamlit

Here is the code for the website

import joblib import matplotlib.pyplot as plt import pandas as pd import streamlit as st model = joblib.load('cancer.pkl') def web_app():        st.write("""        # Breast Cancer Predictor Web App        ## This app predicts whether cancer is Benign or Malignant.         """)        st.header("User Details")        st.subheader("Kindely Enter The following Details in order to make a prediction")        cell_shape = st.number_input("Uniformity of Cell Shape",0,10)        clump_thickness = st.number_input("Clump Thickness",0,10)        cell_size = st.number_input("Uniformity of Cell Size",0,10)        marginal_adheasian = st.number_input("Marginal Adeasion",0,10)        single_epithelial_cell_size = st.number_input("Single Epithelial Cell Size",0,10)        bare_nuclei = st.number_input("Bare Nuclei",0,10)        bland_chromatin = st.number_input("Bland Chromatin",0,10)        normal_nucleoli = st.number_input("Normal Nuceloi",0,10)        mitosis = st.number_input("Mitosis",0,10)                   result = model.predict([[cell_shape,clump_thickness,cell_size,marginal_adheasian,                                                  single_epithelial_cell_size,bare_nuclei,bland_chromatin,                                                  normal_nucleoli,mitosis]])         

if result == 2:

             result = "Benign"         else:               result = "Malignant"        st.text_area(label='Cancer is:- ',value=result , height= 100)      if st.button("Press here to make Prediction"): run = web_app()

That’s how our website looks:

Down below I will be attaching the link to the GITHUB repo.

As we have checked our website is running perfectly time to deploy it on AWS EC2.

How to Deploy your APP on AWS EC2?

Before deploying the website make sure you have an account on AWS. If not, first register and create your account after that follow these steps.

This step is very important because through this step only users from the public world will be able to access it.

Note: Keep this file stored in a folder with this file within your cmd prompt you can also access the server.

Congrats you have created your first EC2 instance.

3. Before entering the EC2 instance, we first need to create a new IAM policy ( IAM stands for Identity Access Management with IAM you can write a policy/role so that one AWS service can access another AWS service). By default, AWS doesn’t allow one AWS service to access another service this is because to avoid any security conflict.

Important Note!! :– We created this new role because in S3 we will be storing our pickle file of the model and in order to get that file inside the EC2 instance we must put the file in S3. S3 stands for Simple Storage Services with these services you can store your data files and later you can use them with various AWS services.

4. Now search for S3 press Alt+F4

8. Now we have configured the changes in AWS EC2. Now it’s time to launch the Instance.

Now install streamlit inside the EC2 using this command:

pip3 install streamlitS

You are currently login as ec2-user. In order to get access to s3, we need to log in as root users. To log in with the root user use this command.

Now go inside your EC2 and follow the below Steps:

2. Go to the EC2 instance then paste the S3 URL inside your EC2

aws s3 cp Your S3 URL/name_of_file name_of_file

aws s3 cp s3://mlwebapp/cancer.pklcCancer.pkl

3. In order to check whether the file is uploaded or not use this command. This command tells the contents inside your directory. This is a Linux command.


4. Now do the same for

5. We also need to install joblib as through the library we are able to load the model that we have trained that is chúng tôi file. And also install the sklearn library as this library contains all the models so install this also

pip3 install joblib

pip3 install sklearn

The final and last step is to run the web-app use this command to run the app.

streamlit run

Copy any of the URLs, for the public world to connect to this app we give External URL, but if we want to check how the app looks then we use the Network URL i.e the private network. After URL has been copied paste that into your new tab.

Now fill in those details to make a prediction

Congratulations on creating an ML web app and deploying it on AWS. Now anyone in the world can use your app.

Here is the GitHub repo for the same, in here I have uploaded all the files including chúng tôi, and the file in which I have trained the model using Logistic Regression. Link to the GitHub repo.


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A Comprehensive Marketing Operations Model: An Essential Part Of The Cmo’s Toolkit

A marketing operations model integrates people, process and technology across the ecosystem, enabling the marketing organization to deliver the right message.

I’ve been working in the Marketing Operations field for almost 15 years now, helping CMOs build operating models to allow their teams, agencies, and martech vendors to work in a coordinated way, leveraging technology and data, complying with policies, and operating within efficient and effective standards.

Indeed, such a model integrates people, process and technology across the ecosystem, enabling the marketing organization to deliver the right message to the intended audience in a multi-channel world. It provides a common framework to deliver the marketing vision and strategy, by breaking down the silos – across departments, functions, business units and geographies.

To achieve the necessary alignment across people, process and technology, key ingredients must be present:

Clear communication of the objectives and benefits of the model.

An environment where all stakeholders across and outside the organization are joined up.

Changes in behaviour to progress from the status quo to a more efficient and effective modus operandi.

Based on the various projects I have personally worked with, and based on the many use cases that are available across the marketing industry, these models exist and there is enough proof that they work well. However, they are not as widespread as you might expect them to be.

The weakness in existing marketing operations models

I was quite surprised recently by a few marketing operations leaders stating that they only focus on rolling out and supporting MarTech platforms, and managing data flows and processes. They further indicated that they do not take on the responsibility of defining marketing processes, policies or change management programs. Why is that? Surely the term “operations” in marketing operations is not purely about the tech stack, right?

As I explain in my eBook Marketing Operations Strategy: Improving the effectiveness of your multichannel marketing programs, and as you can find out by doing a quick online search on the term “marketing operations”, it is defined as the function that coordinates People, Process, and Technology, to enable efficient and effective marketing.

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Based on these definitions, while they are clearly expected to support and maintain the MarTech stack and the data flows and processes, marketing operations teams are also responsible for developing and managing the processes and policies to ensure smooth operation of strategic planning, financial management, marketing performance measurement, marketing infrastructure, marketing and sales alignment, and overall marketing excellence.

In addition, marketing operations teams are responsible for managing learning and development programs to their marketing colleagues, educating them not only on how to use the tools but also on the operating model that has been put in place to optimize performance.

In a nutshell, marketing operations is about providing structure to the marketing organization to deliver programs efficiently, effectively, and within policy. Such alignment requires changes in behaviour, not a very easy remit to deliver on, but an absolutely necessary one.

When the marketing operations team limits its deliverables to rolling out and maintaining the MarTech stack, and they shy away from improving the ways of working and managing change, they end up missing out on the opportunity to optimize the use of the stack. Which is one of the reasons CMOs are often disappointed with their decision to adopt one platform over another.

However, their disappointment is misplaced: while they blame the platform itself, the lack of optimization is often because the team missed out on the opportunity to go through the tough exercise of auditing their processes, identifying areas of improvement and putting in place a plan to change the ways of working.

Market research and trends in the field of marketing operations

These observations are supported by Gartner’s latest report: The Annual CMO Spend Survey 2023-2024 Research where CMOs have reported that they struggle to effectively manage their marketing technology stack. Almost a quarter (24%) of respondents said that marketing technology strategy, adoption, and use is one of their top three weaknesses in their company’s ability to drive customer acquisition or loyalty. More than 25% blamed weaknesses in their martech strategy on insufficient budget, resources or capabilities.

Fixing the issues around marketing operations are top of mind for CMOs and a key focus in the next 18 months. According to the same research from Gartner, while competitive insights and analytics are the two most important capabilities supporting the delivery of marketing strategies, marketing operations is on the rise with 30% of CMOs identifying this area as vital in supporting their strategy.

Most Vital Capabilities Supporting Marketing Strategy

In addition, CMOs estimate that they will spend 12.6% of the marketing budget on Marketing Operations, well ahead of brand strategy/brand building, or sales support/enablement.

Marketing program/operation area spend breakdown

The evolving scope of marketing operations

Gartner’s research clearly shows the importance of marketing operations: planned expenditure areas for 2023 indicate that the function will grow beyond the traditional responsibilities of budgeting, planning and MarTech stack management, to also include skills and capabilities development, ways of working and stakeholder management across a comprehensive ecosystem that will bring together internal teams from across the business as well as external vendors and partners.

This is quite an expansion of the Marketing Operations remit, and the transition to a new operating model may prove challenging to achieve, both in terms of the existing team’s readiness to drive such change, as well as changing the hearts and minds across the ecosystem.

Indeed, the new responsibilities that marketing operations has to take on, require people to change behaviour, how they work, and how they interact with each other, with the ultimate objective of creating an environment that is more transparent and accountable. And how do people react when faced with such changes? They object and refuse to adopt the new practices.

So what’s the solution?

How to build a best-in-class marketing operations function

It’s important to acknowledge upfront that developing and implementing a successful marketing operating model is an effort that will involve stakeholders across the marketing ecosystem, including colleagues within the business, as well as agencies, martech vendors and other partners.

While the marketing operations team may own the framework, they need to collaborate with various stakeholders to define, develop and implement it. However, when introducing change at scale, issues are bound to arise.

As a starting point, be clear on what you want to achieve and get the necessary executive backing

Define your vision, objectives and success metrics.

Get senior executive support to drive transformation – top down support is essential to drive the vision across all levels: while Marketing leadership is a pre-requisite to drive the change, you should also get executive leadership buy-in of other key stakeholders in the marketing process, including Sales, IT, Finance, Procurement, Legal, etc.

Be clear on roles and responsibilities during the change phase and beyond, when the model is fully operational.

Communication is a key ingredient to ensure continuing support

Define your internal communications strategy early on so stakeholders at all levels are kept informed of progress.

Foster a sense of community and knowledge sharing (e.g. success stories) to keep stakeholders engaged.

Manage expectations along the way… success may come in small wins rather than a big bang.

Create an environment of transparency and accountability

Report on progress on a regular basis and an agreed frequency both to the executive team, the change team, key stakeholders and the rest of the organization.

Be honest when things go wrong.

Run post-mortem analysis at major milestones and at the end of the program to learn from mistakes and avoid repeating them at the next stage.

It’s about collaboration, not hierarchy

Engage with and involve stakeholders across the organization.

Use social tools to create a sense of community across all stakeholders.

Upskilling and continuous learning are essential

One core skill the Marketing Operations team will need is to be not only tech enablers but also change agents so make sure the existing resources can take on the challenge of defining and implementing the new framework; where they lack skills, identify opportunities to improve their capabilities.

Upskill the wider marketing team, and other contributors to the marketing process.

Share best practices so colleagues continuously learn and develop.

There are only a handful of recommendations to help CMOs get started on the organizational transformation journey. There are more detailed guidelines in my eBook Marketing Operations Strategy: Improving the effectiveness of your multichannel marketing programs which I hope you will find helpful.

Google On Effect Of Low Quality Pages On Sitewide Rankings

In a Google Webmaster Hangout, someone asked if poor quality pages of a site could drag down the rankings of the entire site. Google’s John Mueller’s answer gave insights into how Google judges and ranks web pages and sites.

Do a Few Pages Drag Down the Entire Site?

The question asked if a section of a site could drag down the rest of the site.

The question:

“I’m curious if content is judged on a page level per the keyword or the site as a whole. Only a sub-section of the site is buying guides and they’re all under their specific URL structure.

Would Google penalize everything under that URL holistically? Do a few bad apples drag down the average?”

Difference Between Not Ranking and Penalization

John Mueller started off by correcting a perception about getting penalized that was inherent in the question. Web publishers sometimes complain about being penalized when in fact they are not. What’s happening is that their page is not ranking.

There is a difference between Google looking at your page and deciding not to rank it.

When a page fails to rank, it’s generally because the content is not good enough (a quality issue) or the content is not relevant to the search query (relevance being to the user). That’s a failure to rank, not a penalization.

A common example is the so-called Duplicate Content Penalty. There is no such penalty. It’s an inability to rank caused by content quality.

Another example is the Content Cannibalization Penalty, which is another so-called penalty that is not a penalty.

A penalty is something completely different in that it is a result of a blatant violation of Google’s guidelines.

John Mueller Defines a Penalty

Google’s Mueller began his answer by first defining what a penalty is:

“Usually the word penalty is associated with manual actions. And if there were a manual action, like if someone manually looked at your website and said this is not a good website then you would have a notification in Search console.

So I suspect that’s not the case…”

How Google Defines Page-Level Quality

Google’s John Mueller appeared to say that Google tries to focus on page quality instead of overall site quality, when it comes to ranking. But he also said this isn’t possible with every website.

Here is what John said:

“In general when it comes to quality of a website we try to be as fine grained as possible to figure out which specific pages or parts of the website are seen as being really good and which parts are kind of maybe not so good.

And depending on the website, sometimes that’s possible. Sometimes that’s not possible. We just have to look at everything overall.”

Why Do Some Sites Get Away with Low Quality Pages?

I suspect, and this is just a guess, that it may be a matter of the density of the low quality noise within the site.

For example, a site might be comprised of high quality web pages but feature a section that contains thin content. In that case, because the thin content is just a single section, it might not interfere with the ability of the pages on the rest of the site from ranking.

In a different scenario, if a site mostly contains low quality web pages, the good quality pages may have a hard time gaining traction through internal linking and the flow of PageRank through the site. The low quality pages could theoretically hinder a high quality page’s ability to acquire the signals necessary for Google to understand the page.

Here is where John described a site that may be unable to rank a high quality page because Google couldn’t get past all the low quality signals.

Here’s what John said:

“So it might be that we found a part of your website where we say we’re not so sure about the quality of this part of the website because there’s some really good stuff here. But there’s also some really shady or iffy stuff here as well… and we don’t know like how we should treat things over all. That might be the case.”

Effect of Low Quality Signals Sitewide

John Mueller offered an interesting insight into how low quality on-page signals could interfere with the ability of high quality pages to rank. Of equal interest he also suggested that in some cases the negative signals might not interfere with the ability of high quality pages to rank.

So if I were to put an idea from this exchange and put it in a bag to take away with me, I’d select the idea that a site with mostly low quality content is going to have a harder time trying to rank a high quality page.

And similarly, a site with mostly high quality content is going to be able to rise above some low quality content that might be separated into it’s own little section. It is of course a good idea to minimize low quality signals as much as you can.

Watch the Webmaster Hangout here.

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The Effect On The Coefficients In The Logistic Regression

Statistically, the connection between a binary dependent variable and one or more independent variables may be modeled using logistic regression. It is frequently used in classification tasks in machine learning and data science applications, where the objective is to predict the class of a new observation based on its attributes. The coefficients linked to each independent variable in logistic regression are extremely important in deciding the model’s result. In this blog article, we’ll look at the logistic regression coefficients and how they affect the model’s overall effectiveness.

Understanding the Logistic Regression Coefficients

It is crucial to comprehend what the logistic regression coefficients stand for before delving into their impact. To measure the link between each independent variable and the dependent variable, logistic regression uses coefficients. When all other variables are held constant, they show how the dependent variable’s log odds change as the corresponding independent variable increases by one unit. The logistic regression equation has the following mathematical form −

$$mathrm{log(p/1-p) = β0 + β1X1 + β2X2 + … + βnXn}$$

where the intercept is 0 and the coefficients for each independent variable (X1 to Xn) are 1 to n, and p is the probability of the dependent variable (usually shown as 0 or 1).

Effect of the Coefficients on Logistic Regression

In logistic regression, the coefficients are critical in deciding the model’s result. The logistic curve’s form, in turn, impacts the anticipated probability, depending on the size and sign of the coefficients. Let’s look more closely at how the coefficients affect the logistic regression model.

1. Magnitude of Coefficient

The magnitude of the coefficients in logistic regression indicates how closely the independent and dependent variables are connected. With a larger coefficient, the correlation between the independent and dependent variables is stronger. On the other hand, when the coefficient is lower, the link between the independent and dependent variables is weaker. Or, to put it another way, a little change in an independent variable with a large coefficient can have a tremendous impact on the predicted likelihood.

2. Sign of the Coefficients

The direction of the link between the independent and dependent variables in logistic regression is shown by the sign of the coefficients. An increasing independent variable enhances the chance of the dependent variable, which is shown by a positive coefficient. As the independent, variable rises, the likelihood of the dependent variable falls, which is shown by a negative coefficient.

3. Interpretation of the Coefficients

With logistic regression, the coefficients must be interpreted considerably differently than for linear regression. As the independent variable grows by one unit, the dependent variable also changes, as seen by the coefficients in linear regression. The log odds of the dependent variable change in contrast to an increase of one unit in the independent variable, according to the logistic regression coefficients. Understanding how the coefficients impact the model’s predictions is important, even though this interpretation could be a bit difficult.


In logistic regression, the coefficients are critical in deciding the model’s result. They aid in determining the anticipated probability and quantifying the link between the independent and dependent variables. The performance and predicted accuracy of the logistic regression model can be enhanced by comprehending the impact of the coefficients. In conclusion, it is crucial to carefully analyze the significance of the size and sign of the coefficients in logistic regression in order to create a successful model.

The Threat Of Misusing Stolen Card Data: An Introduction To Carding Attacks

Organizations face a wide range of cyberattacks. Some, like Denial of Service (DoS) and ransomware attacks, are designed to be destructive, while others are intended to steal sensitive information for the attacker’s use or resale.

Inside the Carding Attack Lifecycle

Carding attacks are only one step in an attack’s lifecycle. Before cybercriminals can test the validity of a list of credit card numbers, they need to have a list to test. A list of validated credit card numbers is typically not the end goal of the attack, so additional stages exist after carding to make use of the new list.  

Before Carding: Card Number Theft

Carding attacks are designed to weed out incorrect credit card information or those that have expired or been cancelled from valid ones. Before performing a carding attack, a cybercriminal needs a list of potential credit card numbers to test. A number of different ways exist for an attacker to gather this information. Many companies collect this type of payment card data in order to autofill payment information for online purchases or for automatic billing (healthcare providers, utilities, etc.). A method for collecting credit card data that has become popular in recent years is credit card skimming. Credit card skimmers exist almost anywhere that credit cards are used. Physical devices are placed on gas pumps and ATMs, skimming malware is installed on point of sale (PoS) terminals in stores (which enabled

The Carding Attack

The problem with lists of credit card numbers is that the cybercriminal may not know their provenance. A list purchased from another criminal may include all new numbers or aggregate numbers from past breaches. If the latter is true, many of these cards may have been cancelled as part of the breach remediation efforts. Additionally, the cybercriminal may not have full card information, including the PIN number needed for online purchases. Carding attacks are designed to fix this problem. Most credit card PINs are three digits long, meaning that there are 1,000 possible values, which is an entirely guessable and testable number. Many sites may have a mechanism in place to prevent a user from trying 1,000 different payments with the same card but different PIN numbers. However, these sites probably don’t coordinate. If the threshold for mistakes is five attempts per card, then a cybercriminal only requires 200 payment portals to brute-force a card’s PIN number (and probably less on average).  

Impacts of Carding

Carding attacks are profitable for an attacker since they produce a list of verified and validated credit cards. These fetch a much higher price on the black market since they are guaranteed to work if used shortly after validation. Validated credit cards are extremely useful for online shopping. Once an item has been purchased and shipped by the retailer, the seller has no control over it. As a result, there is no chance of the cybercriminal losing the item even if the owner of the card notices the anomalous transaction and reverses the charge. With credit card fraud and carding attacks, it is most likely the merchant that pays the price. Credit card companies will reverse a disputed transaction (called a chargeback), meaning that the retailer loses both their inventory and the payment for it.  

Protecting Against Carding Attacks

Carding attacks can have a significant impact on a merchant’s bottom line. If they are the victim of credit card fraud, they may lose significant amounts of money in chargebacks. On the other hand, if they are one of the sites used in carding attacks, they have their resources wasted by the thousands or millions of fake transactions being performed by cybercriminals attempting to validate a list of credit card information. The nature of carding attacks makes it relatively easy to detect on a merchant’s website. The site will experience a high number of payment attempts with many failed transactions. This will also include a high rate of cart abandonment if a purchase is designed only to validate a particular card and is abandoned once verification occurs. These attacks are also commonly performed by bots (due to their repetitive and time-consuming nature), and bots often have features that help to differentiate them from human users.

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